Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vinnie357/claude-skills --skill graphify-agentsgit clone --depth 1 https://github.com/vinnie357/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vinnie357/claude-skills/graphify-agents)<a href="https://agentmods.dev/skills/vinnie357/claude-skills/graphify-agents"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/graphify-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vinnie357/claude-skills/graphify-agents"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/graphify-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.01154 |
| Opus 5 | $0.00037 | $0.00577 |
| Sonnet 5 | $0.00015 | $0.00231 |
| Haiku 4.5 | $0.00007 | $0.00115 |
Grade A, and why
graphify-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graphify for Agents
Reading raw files into context is the dominant token cost of codebase-understanding work. Graphify builds a knowledge graph once (graphify-out/graph.json) and answers structural questions by traversing it — returning the relevant nodes and edges instead of whole files. This skill covers using that graph to make agent workflows cheaper and more targeted.
The project's published benchmark reports "71.5x fewer tokens per query vs reading raw files" on a mixed corpus, with smaller gains (≈5.4x on a 4-file corpus, ≈1x on a 6-file library) — the advantage scales with corpus size. Measure a specific repo with graphify benchmark before quoting a number; small repos see little benefit, so reserve the graph for large corpora.
For installing graphify and the full CLI surface, load the graphify skill.
The Core Pattern: Query, Don't Read
| Question shape | Without graphify | With graphify |
|---|---|---|
| "How does auth connect to the request pipeline?" | Grep, open 6–10 files, read each | graphify query "how does auth connect to the request pipeline?" → relevant nodes + edges |
"What is SwinTransformer and what touches it?" |
Read the class + every caller | graphify explain "SwinTransformer" |
"What breaks if I change add?" |
Trace callers by hand | graphify affected "add" |
| "How do these two modules connect?" | Read both, infer | graphify path "DigestAuth" "Response" |
query returns within a token budget (--budget, default 2000), so an agent gets a bounded, relevant slice rather than unbounded file contents.
Integration with /core:agent-loop
Graphify slots into the loop's existing phases without replacing them. Load /core:agent-loop for the phase model.
Phase 1 (Pre-flight / research). Before decomposition, build or refresh the graph, then have the research/Explore subagent query it instead of fanning out file reads:
mise run graphify:update # AST-only refresh, no API key needed
graphify query "where is <epic-area> implemented and what does it depend on?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 74 lines · 74 tokens per session scan A 433298100d17
graphify-agents is a skill published in the GitHub repository vinnie357/claude-skills (25 stars, last pushed 3d ago), licensed MIT. It adds 74 tokens to every session and 1,154 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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